I like to travel, despite all the bad luck that usually follows me wherever I choose to go. In 2010, I joined the Faculty of Letters and the Graduate Program in Linguistics at the Federal University of Juiz de Fora, where I teach both undergraduate and graduate division courses on Cognitive Linguistics, Computational Linguistics and Historical Linguistics. More recently, our lab got funded to develop collaborative research on contrastive Frame Semantics and Construction Grammar for Machine Translation with the groups led by Alexander Ziem, from the University of Dusseldorf, and Oliver Czulo, from the University of Leipzig, I've also been collaborating with Ben Lyngfelt and Lars Borin, from the University of Gothenburg, in an effort for contrasting and aligning constructions in Brazilian Portuguese and Swedish. Since then, I've been coming back to Bay Area very frequently due to intensive collaboration with the Berkeley FrameNet folks in several projects, the latest being the Multilingual FrameNet initiative, which has been using the FrameNet Brasil WebTool for an experiment of joint annotation of full texts in several languages. During my PhD, I spent one semester in Berkeley at the Linguistics Department of the University of California, under the supervision of Professor Eve Sweetser. I've received my PhD in Linguistics in 2009 from the Federal University of Rio de Janeiro, working with Professor Maria Luiza Braga on the relation between inheritance and change using a model to account for the diachronic reorganization of Construction Networks due to the convergence of new patterns into families of constructions. I'm also one of the enthusiasts for the Global FrameNet initiative. Currently, those applications include a Constructional Analyzer using Relations Among Multiple Attribute-Value Matrices (CARMA) and a Multilingual Knowledge Base (m.knob) for Tourism. I'm the PI of the FrameNet Brasil Computational Linguistics Lab at the Federal University of Juiz de Fora, Brazil, where I've been leading, together with Ely Matos, a team of about 20 people interested in developing a FrameNet for Brazilian Portuguese (FrameNet Brasil) and cognitively inspired applications for Natural Language Understanding based on it.
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It is postulated that the observed yield, denotedīy, is a noisy version of this unobserved (true) yield. Finally, note that the coefficient of in the third term is a unimodal function of that decays monotonically to 0 as and as therefore the third term is associated with the medium term bond yields. Next, note that as the coefficient of in the second term converges to 1 while that of in the third term converges to 0 therefore the second term can be thought of as a correction to the overall yield that isĪssociated with the short term bonds. It can also be thought of as the long term yield because as the other two terms vanish the coefficients of both and converge to 0 as (recall that is positive). The first term can be thought of as the overall yield level because it does not depend on, the bond maturity. For fixed time period t, the three terms in this model have relatively simple interpretation. This model is a dynamic version of a static model discussed in Nelson and Siegel ( 1987), where and are time invariant. One of these models depends on a positive, time-varying, scalar parameter and a time-varying three-dimensional vector parameter. Koopman, Mallee, and van der Wel ( 2010) discuss a variety of models for, which is called the yield surface. Even if time is not measured continuously and the bonds of only certain maturities are traded, is treated as a smooth function of two continuous variables, time t and maturity. Suppose that denotes the (idealized) yield at time t that is associated with a bond of maturity (in months). Values for this new bond, which is assigned the index of 18-that is, the value of mtype is 18. The following DATA step creates the necessary missing Has a maturity of 42 months and is not traded on the general exchange. In addition, suppose you are interested in extrapolating the fitted model to predict the yield of a hypothetical bond that The data have been extendedįor two more years by adding missing yields for the years 20, which causes the SSM procedure to produce model forecasts The variable date contains the observation date, yield contains the bond yield, maturity contains the associated bond maturity, and mtype contains an index (ranging from 1 to 17) that sequentially labels bonds of increasing maturity. The data are monthly bond yields that were recorded between the start of 1970 to the end ofĢ000 for 17 bonds of different maturities the maturities range from three months to 10 years (120 months). The following DATA step creates the yield-curve data set, Dns, that is used in this article. 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